# Recorder

Support for round trips of TF data to annotation tools and back.

The scenario is:

• Prepare a piece of corpus material for plain text use in an annotation tool, e.g. BRAT.
• Alongside the plain text, generate a mapping file that maps nodes to character positions in the plain text
• Use an annotation tool to annotate the plain text
• Read the output of the annotation tools and convert it into TF features, using the mapping file.

## Explanation

The recorder object is an engine to which you can send text material, interspersed with commands that say:

• start node n;
• end node n.

The recorder stores the accumulating text as a plain text, without any trace of the start and end commands. However, it also maintains a mapping between character positions in the accumulated text and the nodes.

At any moment, there is a set of active nodes: the ones that have been started, but not yet ended.

Every character of text that has been sent to the recorder will add an entry to the position mapping: it maps the position of that character to the set of active nodes at that point.

## Usage

We suppose you have a corpus loaded, either by

from tf.app import use
A = use(corpus)
api = A.api


or by

from tf.fabric import Fabric
TF = Fabric(locations, modules)

from tf.convert.recorder import Recorder

rec = Recorder(api)

rec.start(n1)

rec.start(n2)

rec.end(n1)

rec.end(n2)



This leads to the following mapping:

position text active nodes
0 a {}
1 b {n1}
2 c {n1}
3 d {n1, n2}
4 e {n1, n2}
5 f {n1, n2}
6 g {n2}
7 h {n2}
8 i {n2}
9 j {n2}
10 k {}
11 l {}
12 m {}
13 n {}
14 o {}

There are methods to obtain the accumulated text and the mapped positions from the recorder.

You can write the information of a recorder to disk and read it back later.

And you can generate features from a CSV file using the mapped positions.

To see it in action, see this tutorial

Expand source code Browse git
"""
# Recorder

Support for round trips of TF data to annotation tools and back.

The scenario is:

*   Prepare a piece of corpus material for plain text use in an annotation tool,
e.g. [BRAT](https://brat.nlplab.org).
*   Alongside the plain text, generate a mapping file that maps nodes to
character positions in the plain text
*   Use an annotation tool to annotate the plain text
*   Read the output of the annotation tools and convert it into TF features,
using the mapping file.

## Explanation

The recorder object is an engine to which you can send text material, interspersed
with commands that say:

* start node n;
* end node n.

The recorder stores the accumulating text as a plain text, without any
trace of the start and end commands.
However, it also maintains a mapping between character positions in the
accumulated text and the nodes.

At any moment, there is a set of *active* nodes: the ones that have been started,
but not yet ended.

Every character of text that has been sent to the recorder
will add an entry to the position mapping: it maps the position of that character
to the set of active nodes at that point.

## Usage

We suppose you have a corpus loaded, either by


from tf.app import use
A = use(corpus)
api = A.api


or by


from tf.fabric import Fabric
TF = Fabric(locations, modules)



from tf.convert.recorder import Recorder

rec = Recorder(api)

rec.start(n1)

rec.start(n2)

rec.end(n1)

rec.end(n2)



This leads to the following mapping:

position | text | active nodes
--- | --- | ---
0 | a | {}
1 | b | {n1}
2 | c | {n1}
3 | d | {n1, n2}
4 | e | {n1, n2}
5 | f | {n1, n2}
6 | g | {n2}
7 | h | {n2}
8 | i | {n2}
9 | j | {n2}
10 | k | {}
11 | l | {}
12 | m | {}
13 | n | {}
14 | o | {}

There are methods to obtain the accumulated text and the mapped positions from the
recorder.

You can write the information of a recorder to disk and read it back later.

And you can generate features from a CSV file using the mapped positions.

To see it in action, see this
[tutorial](https://nbviewer.jupyter.org/github/etcbc/bhsa/blob/master/tutorial/annotate.ipynb)
"""

import os
from itertools import chain

from ..core.helpers import (
specFromRangesLogical,
specFromRanges,
rangesFromSet,
expanduser,
)

ZWJ = "\u200d"  # zero width joiner

class Recorder:
def __init__(self, api=None):
"""Accumulator of generated text that remembers node positions.

Parameters
----------
api: obj, optional None
The handle of the API of a loaded TF corpus.
This is needed for operations where the recorder needs
TF intelligence associated with the nodes, e.g. their types.
If you do not pass an api, such methods are unavailable later on.
"""
self.api = api

self.material = []
"""Accumulated text.

It is a list of chunks of text.
The text is just the concatenation of all chunks.
"""

self.nodesByPos = []
"""Mapping from textual positions to nodes.

It is a list. Entry p in this list stores node information
for character position p.
That node information consists of the set of active nodes
at that position.
"""

self.context = set()
"""The currently active nodes.
"""

def start(self, n):
"""Start a node.

That means: add it to the context, i.e. make the node active.

Parameters
----------
n: integer
A node. The node can be any node type.
"""

def end(self, n):
"""End a node.

That means: delete it from the context, i.e. make the node inactive.

Parameters
----------
n: integer
A node. The node can be any node type.
"""

Parameters
----------
string: string | None
If it is a string, the string will be added to the accumulator.
If it is None, a default value will be added.
The default value is passed through parameter empty.
empty: string, optional zero-width-joiner
If the string parameter is None, this is the default value
that will be added to the accumulator.
If this parameter is absent, the zero-width joiner is used.
"""

if string is None:
string = empty
self.material.append(string)
self.nodesByPos.extend([frozenset(self.context)] * len(string))

def text(self):
"""Get the accumulated text.

Returns
-------
string
The join of all accumulated text chunks.
"""
return "".join(self.material)

def positions(self, byType=False, simple=False):
"""Get the node positions as mapping from character positions.

Parameters
----------
byType: boolean, optional False
If True, makes a separate node mapping per node type.
For this it is needed that the Recorder has been
passed a TF api when it was initialized.
simple: boolean, optional False
In some cases it is known on beforehand that at each textual position
there is at most 1 node.
Then it is more economical to fill the list with single nodes
rather than with sets of nodes.
If this parameter is True, we pick the first node from the set.

Returns
-------
list|dict|None
If byType, the result is a dictionary, keyed by node type,
with values the mapping of textual positions to nodes of that type.
This mapping takes the shape of a list where entry i
contains the frozen set of all nodes of that type
that were active at character position i in the text.

If not byType then a single mapping is returned (as list),
where entry i contains the frozen set of all
nodes, irrespective of their type
that were active at character position i in the text.
"""

if not byType:
if simple:
return tuple(list(x)[0] if x else None for x in self.nodesByPos)
return self.nodesByPos

api = self.api
if api is None:
print(
"""\
Cannot determine node types without a TF api.
You have to call Recorder(api) instead of Recorder()
where api is the result of
tf.app.use(corpus)
or
"""
)
return None

F = api.F
Fotypev = F.otype.v
info = api.TF.info
indent = api.TF.indent

indent(level=True, reset=True)
info("gathering nodes ...")

allNodes = set(chain.from_iterable(self.nodesByPos))
allTypes = {Fotypev(n) for n in allNodes}
info(f"found {len(allNodes)} nodes in {len(allTypes)} types")

nodesByPosByType = {nodeType: [] for nodeType in allTypes}

info("partitioning nodes over types ...")

for nodeSet in self.nodesByPos:
typed = {}
for node in nodeSet:
nodeType = Fotypev(node)
for nodeType in allTypes:
thisSet = (
frozenset(typed[nodeType]) if nodeType in typed else frozenset()
)
value = (list(thisSet)[0] if thisSet else None) if simple else thisSet
nodesByPosByType[nodeType].append(value)

info("done")
indent(level=False)
return nodesByPosByType

def iPositions(self, byType=False, logical=True, asEntries=False):
"""Get the character positions as mapping from nodes.

Parameters
----------
byType: boolean, optional False
If True, makes a separate node mapping per node type.
For this it is needed that the Recorder has been
passed a TF api when it was initialized.
logical: boolean, optional True
If True, specs are represented as tuples of ranges
and a range is represented as a tuple of a begin and end point,
or as a single point.
Points are integers.
If False, ranges are represented by strings: , separated ranges,
a ranges is b-e or p.
asEntries: boolean, optional False
If True, do not return the dict, but rather its entries.

Returns
-------
list|dict|None
If byType, the result is a dictionary, keyed by node type,
with values the mapping for nodes of that type.
Entry n in this mapping contains the intervals of all
character positions in the text where node n is active.

If not byType then a single mapping is returned, where each node
is mapped to the intervals where that node is active.
"""

method = specFromRangesLogical if logical else specFromRanges
posByNode = {}
for (i, nodeSet) in enumerate(self.nodesByPos):
for node in nodeSet:
for (n, posSet) in posByNode.items():
posByNode[n] = method(rangesFromSet(posSet))

if asEntries:
posByNode = tuple(posByNode.items())
if not byType:
return posByNode

api = self.api
if api is None:
print(
"""\
Cannot determine node types without a TF api.
You have to call Recorder(api) instead of Recorder()
where api is the result of
tf.app.use(corpus)
or
"""
)
return None

F = api.F
Fotypev = F.otype.v

posByNodeType = {}
if asEntries:
for (n, spec) in posByNode:
nType = Fotypev(n)
posByNodeType.setdefault(nType, []).append((n, spec))
else:
for (n, spec) in posByNode.items():
nType = Fotypev(n)
posByNodeType.setdefault(nType, {})[n] = spec

return posByNodeType

def rPositions(self, acceptMaterialOutsideNodes=False):
"""Get the first textual position for each node

The position information is a big amount of data, in the general case.
Under certain assumptions we can economize on this data usage.

Strong assumptions:

1.  every textual position is covered by **exactly one node**
2.  the nodes are consecutive:
every next node is equal to the previous node plus 1
3.  the positions of the nodes are monotonous in the nodes, i.e.
if node n < m, then the position of n is before the position of m.

Imagine the text partitioned in consecutive non-overlapping chunks, where
each node corresponds to exactly one chunk, and the order of the nodes
is the same as the order of the corresponding chunks.

Parameters
----------
acceptMaterialOutsideNodes: boolean, optional False
If this is True, we accept that the text contains extra material that is not
covered by any node.
So condition 1 above is relaxed in that we accept no nodes for a textual
position.

Applications that make use of the positions must realize that in this case
the material associated with a node also includes the subsequent material
outside any node.

Returns
-------
list | str
The result is a list posList.
Suppose we map nodes n, n+1, ..., n+m.
Suppose node n starts at position t0, n+1 at position t1,
n+m at position tm.
Suppose te is the position just after the whole text.

Then we deliver as a result:


n - 1
t0
t1
...
tm
te


So the first element of the list is used to specify the offset to be
applied for all subsequent nodes.
The te value is added as a sentinel, to facilitate the determination
of the last position of each node.

Users of posList can find the start and end positions of node m
as follows


start = posList[m - posList[0]]
end   = posList[m - posList[0] + 1] - 1


We return not only the posList if the assumptions hold.
If not, we return a string with diagnostic information.
"""

good = True
multipleNodes = 0
multipleFirst = 0
noNodes = 0
noFirst = 0
nonConsecutive = 0
nonConsecutiveFirst = 0

posByNode = {}
for (i, nodeSet) in enumerate(self.nodesByPos):
if (not acceptMaterialOutsideNodes and len(nodeSet) == 0) or len(
nodeSet
) > 1:
good = False
if len(nodeSet) == 0:
if noNodes == 0:
noFirst = i
noNodes += 1
else:
if multipleNodes == 0:
multipleFirst = i
multipleNodes += 1
continue
for node in nodeSet:
if node in posByNode:
continue
posByNode[node] = i

lastI = i

if not good:
msg = ""
if noNodes:
msg += (
f"{noNodes} positions without node, "
f"of which the first one is {noFirst}\n"
)
if multipleNodes:
msg += (
f"{multipleNodes} positions with multiple nodes, "
f"of which the first one is {multipleFirst}\n"
)
return msg

sortedPosByNode = sorted(posByNode.items())
offset = sortedPosByNode[0][0] - 1
posList = [offset]
prevNode = offset
for (node, i) in sortedPosByNode:
if prevNode + 1 != node:
good = False
if nonConsecutive == 0:
nonConsecutiveFirst = f"{prevNode} => {node}"
nonConsecutive += 1
else:
posList.append(i)
prevNode = node
posList.append(lastI)

if not good:
return (
f"{nonConsecutive} nonConsecutive nodes, "
f"of which the first one is {nonConsecutiveFirst}"
)
return posList

def write(
self, textPath, inverted=False, posPath=None, byType=False, optimize=True
):
"""Write the recorder information to disk.

The recorded text is written as a plain text file,
and the remembered node positions are written as a tsv file.

You can also let the node positions be written out by node type.
In that case you can also optimize the file size.

Optimization means that consecutive equal values are prepended
by the number of repetitions and a *.

Parameters
----------
textPath: string
The file path to which the accumulated text is written.
inverted: boolean, optional False
If False, the positions are taken as mappings from character
positions to nodes. If True, they are a mapping from nodes to
character positions.
posPath: string, optional None
The file path to which the mapped positions are written.
If absent, it equals textPath with .pos appended, or
.ipos if inverted is True.
The file format is: one line for each character position,
on each line a tab-separated list of active nodes.
byType: boolean, optional False
If True, writes separate node mappings per node type.
For this it is needed that the Recorder has been
passed a TF api when it was initialized.
The file names are extended with the node type.
This extension occurs just before the last . of the inferred posPath.
optimize: boolean, optional True
Optimize file size. Only relevant if byType is True
and inverted is False.
The format of each line is:

*rep* * *nodes

where *rep* is a number that indicates repetition and *nodes*
is a tab-separated list of node numbers.

The meaning is that the following *rep* character positions
are associated with these *nodes*.
"""

textPath = expanduser(textPath)
posExt = ".ipos" if inverted else ".pos"
posPath = expanduser(posPath or f"{textPath}{posExt}")

with open(textPath, "w", encoding="utf8") as fh:
fh.write(self.text())

if not byType:
with open(posPath, "w", encoding="utf8") as fh:
if inverted:
fh.write(
"\n".join(
f"{node}\t{intervals}"
for (node, intervals) in self.iPositions(
byType=False, logical=False, asEntries=True
)
)
)
else:
fh.write(
"\n".join(
"\t".join(str(i) for i in nodes)
for nodes in self.nodesByPos
)
)
return

mapByType = (
self.iPositions(byType=True, logical=False, asEntries=True)
if inverted
else self.positions(byType=True)
)
if mapByType is None:
print("No position files written")
return

(base, ext) = os.path.splitext(posPath)

# if we reach this, there is a TF api

api = self.api
info = api.TF.info
indent = api.TF.indent

indent(level=True, reset=True)

for (nodeType, mapping) in mapByType.items():
fileName = f"{base}-{nodeType}{ext}"
info(f"{nodeType:<20} => {fileName}")
with open(fileName, "w", encoding="utf8") as fh:
if inverted:
fh.write(
"\n".join(
f"{node}\t{intervals}" for (node, intervals) in mapping
)
)
else:
if not optimize:
fh.write(
"\n".join(
"\t".join(str(i) for i in nodes) for nodes in mapping
)
)
else:
repetition = 1
previous = None

for nodes in mapping:
if nodes == previous:
repetition += 1
continue
else:
if previous is not None:
prefix = f"{repetition}*" if repetition > 1 else ""
value = "\t".join(str(i) for i in previous)
fh.write(f"{prefix}{value}\n")
repetition = 1
previous = nodes
if previous is not None:
prefix = f"{repetition + 1}*" if repetition else ""
value = "\t".join(str(i) for i in previous)
fh.write(f"{prefix}{value}\n")

indent(level=False)

Parameters
----------
textPath: string
The file path from which the accumulated text is read.
posPath: string, optional None
The file path from which the mapped positions are read.
If absent, it equals textPath with .pos appended.
The file format is: one line for each character position,
on each line a tab-separated list of active nodes.
"""

textPath = expanduser(textPath)
posPath = expanduser(posPath or f"{textPath}.pos")
self.context = {}

with open(textPath, encoding="utf8") as fh:
self.material = list(fh)

with open(posPath, encoding="utf8") as fh:
self.nodesByPos = [
{int(n) for n in line.rstrip("\n").split("\t")} for line in fh
]

"""Read a tab-separated file of annotation data and convert it to features.

An external annotation tool typically annotates text by assigning values
to character positions or ranges of character positions.

In Text-Fabric, annotations are values assigned to nodes.

If a *recorded* text has been annotated by an external tool,
we can use the position-to-node mapping to construct Text-Fabric features
out of it.

The annotation file is assumed to be a tab-separated file.
Every line corresponds to an annotation.
The first two columns have the start and end positions, as character positions
in the text.
The remaining columns contain annotation values for that strectch of text.

If there is a heading column, the values of the headers translate to names
of the new TF features.

Parameters
----------
featurePath: string
Path to the annotation file.
headers: boolean or iterable, optional True
Indicates whether the annotation file has headers.
If not True, it may be an iterable of names, which will
"""

featurePath = expanduser(featurePath)
nodesByPos = self.nodesByPos

features = {}

with open(featurePath, encoding="utf8") as fh:
names = next(fh).rstrip("\n").split("\t")[2:]
else:
names = None

for line in fh:
(start, end, *data) = line.rstrip("\n").split("\t")
if names is None:
names = tuple(f"f{i}" for i in range(1, len(data) + 1))
nodes = set(
chain.from_iterable(
nodesByPos[i] for i in range(int(start), int(end) + 1)
)
)
for n in nodes:
for i in range(len(names)):
val = data[i]
if not val:
continue
name = names[i]
features.setdefault(name, {})[n] = val

return features

## Classes

 class Recorder (api=None) 

Accumulator of generated text that remembers node positions.

## Parameters

api : obj, optional None
The handle of the API of a loaded TF corpus. This is needed for operations where the recorder needs TF intelligence associated with the nodes, e.g. their types. If you do not pass an api, such methods are unavailable later on.
Expand source code Browse git
class Recorder:
def __init__(self, api=None):
"""Accumulator of generated text that remembers node positions.

Parameters
----------
api: obj, optional None
The handle of the API of a loaded TF corpus.
This is needed for operations where the recorder needs
TF intelligence associated with the nodes, e.g. their types.
If you do not pass an api, such methods are unavailable later on.
"""
self.api = api

self.material = []
"""Accumulated text.

It is a list of chunks of text.
The text is just the concatenation of all chunks.
"""

self.nodesByPos = []
"""Mapping from textual positions to nodes.

It is a list. Entry p in this list stores node information
for character position p.
That node information consists of the set of active nodes
at that position.
"""

self.context = set()
"""The currently active nodes.
"""

def start(self, n):
"""Start a node.

That means: add it to the context, i.e. make the node active.

Parameters
----------
n: integer
A node. The node can be any node type.
"""

def end(self, n):
"""End a node.

That means: delete it from the context, i.e. make the node inactive.

Parameters
----------
n: integer
A node. The node can be any node type.
"""

Parameters
----------
string: string | None
If it is a string, the string will be added to the accumulator.
If it is None, a default value will be added.
The default value is passed through parameter empty.
empty: string, optional zero-width-joiner
If the string parameter is None, this is the default value
that will be added to the accumulator.
If this parameter is absent, the zero-width joiner is used.
"""

if string is None:
string = empty
self.material.append(string)
self.nodesByPos.extend([frozenset(self.context)] * len(string))

def text(self):
"""Get the accumulated text.

Returns
-------
string
The join of all accumulated text chunks.
"""
return "".join(self.material)

def positions(self, byType=False, simple=False):
"""Get the node positions as mapping from character positions.

Parameters
----------
byType: boolean, optional False
If True, makes a separate node mapping per node type.
For this it is needed that the Recorder has been
passed a TF api when it was initialized.
simple: boolean, optional False
In some cases it is known on beforehand that at each textual position
there is at most 1 node.
Then it is more economical to fill the list with single nodes
rather than with sets of nodes.
If this parameter is True, we pick the first node from the set.

Returns
-------
list|dict|None
If byType, the result is a dictionary, keyed by node type,
with values the mapping of textual positions to nodes of that type.
This mapping takes the shape of a list where entry i
contains the frozen set of all nodes of that type
that were active at character position i in the text.

If not byType then a single mapping is returned (as list),
where entry i contains the frozen set of all
nodes, irrespective of their type
that were active at character position i in the text.
"""

if not byType:
if simple:
return tuple(list(x)[0] if x else None for x in self.nodesByPos)
return self.nodesByPos

api = self.api
if api is None:
print(
"""\
Cannot determine node types without a TF api.
You have to call Recorder(api) instead of Recorder()
where api is the result of
tf.app.use(corpus)
or
"""
)
return None

F = api.F
Fotypev = F.otype.v
info = api.TF.info
indent = api.TF.indent

indent(level=True, reset=True)
info("gathering nodes ...")

allNodes = set(chain.from_iterable(self.nodesByPos))
allTypes = {Fotypev(n) for n in allNodes}
info(f"found {len(allNodes)} nodes in {len(allTypes)} types")

nodesByPosByType = {nodeType: [] for nodeType in allTypes}

info("partitioning nodes over types ...")

for nodeSet in self.nodesByPos:
typed = {}
for node in nodeSet:
nodeType = Fotypev(node)
for nodeType in allTypes:
thisSet = (
frozenset(typed[nodeType]) if nodeType in typed else frozenset()
)
value = (list(thisSet)[0] if thisSet else None) if simple else thisSet
nodesByPosByType[nodeType].append(value)

info("done")
indent(level=False)
return nodesByPosByType

def iPositions(self, byType=False, logical=True, asEntries=False):
"""Get the character positions as mapping from nodes.

Parameters
----------
byType: boolean, optional False
If True, makes a separate node mapping per node type.
For this it is needed that the Recorder has been
passed a TF api when it was initialized.
logical: boolean, optional True
If True, specs are represented as tuples of ranges
and a range is represented as a tuple of a begin and end point,
or as a single point.
Points are integers.
If False, ranges are represented by strings: , separated ranges,
a ranges is b-e or p.
asEntries: boolean, optional False
If True, do not return the dict, but rather its entries.

Returns
-------
list|dict|None
If byType, the result is a dictionary, keyed by node type,
with values the mapping for nodes of that type.
Entry n in this mapping contains the intervals of all
character positions in the text where node n is active.

If not byType then a single mapping is returned, where each node
is mapped to the intervals where that node is active.
"""

method = specFromRangesLogical if logical else specFromRanges
posByNode = {}
for (i, nodeSet) in enumerate(self.nodesByPos):
for node in nodeSet:
for (n, posSet) in posByNode.items():
posByNode[n] = method(rangesFromSet(posSet))

if asEntries:
posByNode = tuple(posByNode.items())
if not byType:
return posByNode

api = self.api
if api is None:
print(
"""\
Cannot determine node types without a TF api.
You have to call Recorder(api) instead of Recorder()
where api is the result of
tf.app.use(corpus)
or
"""
)
return None

F = api.F
Fotypev = F.otype.v

posByNodeType = {}
if asEntries:
for (n, spec) in posByNode:
nType = Fotypev(n)
posByNodeType.setdefault(nType, []).append((n, spec))
else:
for (n, spec) in posByNode.items():
nType = Fotypev(n)
posByNodeType.setdefault(nType, {})[n] = spec

return posByNodeType

def rPositions(self, acceptMaterialOutsideNodes=False):
"""Get the first textual position for each node

The position information is a big amount of data, in the general case.
Under certain assumptions we can economize on this data usage.

Strong assumptions:

1.  every textual position is covered by **exactly one node**
2.  the nodes are consecutive:
every next node is equal to the previous node plus 1
3.  the positions of the nodes are monotonous in the nodes, i.e.
if node n < m, then the position of n is before the position of m.

Imagine the text partitioned in consecutive non-overlapping chunks, where
each node corresponds to exactly one chunk, and the order of the nodes
is the same as the order of the corresponding chunks.

Parameters
----------
acceptMaterialOutsideNodes: boolean, optional False
If this is True, we accept that the text contains extra material that is not
covered by any node.
So condition 1 above is relaxed in that we accept no nodes for a textual
position.

Applications that make use of the positions must realize that in this case
the material associated with a node also includes the subsequent material
outside any node.

Returns
-------
list | str
The result is a list posList.
Suppose we map nodes n, n+1, ..., n+m.
Suppose node n starts at position t0, n+1 at position t1,
n+m at position tm.
Suppose te is the position just after the whole text.

Then we deliver as a result:


n - 1
t0
t1
...
tm
te


So the first element of the list is used to specify the offset to be
applied for all subsequent nodes.
The te value is added as a sentinel, to facilitate the determination
of the last position of each node.

Users of posList can find the start and end positions of node m
as follows


start = posList[m - posList[0]]
end   = posList[m - posList[0] + 1] - 1


We return not only the posList if the assumptions hold.
If not, we return a string with diagnostic information.
"""

good = True
multipleNodes = 0
multipleFirst = 0
noNodes = 0
noFirst = 0
nonConsecutive = 0
nonConsecutiveFirst = 0

posByNode = {}
for (i, nodeSet) in enumerate(self.nodesByPos):
if (not acceptMaterialOutsideNodes and len(nodeSet) == 0) or len(
nodeSet
) > 1:
good = False
if len(nodeSet) == 0:
if noNodes == 0:
noFirst = i
noNodes += 1
else:
if multipleNodes == 0:
multipleFirst = i
multipleNodes += 1
continue
for node in nodeSet:
if node in posByNode:
continue
posByNode[node] = i

lastI = i

if not good:
msg = ""
if noNodes:
msg += (
f"{noNodes} positions without node, "
f"of which the first one is {noFirst}\n"
)
if multipleNodes:
msg += (
f"{multipleNodes} positions with multiple nodes, "
f"of which the first one is {multipleFirst}\n"
)
return msg

sortedPosByNode = sorted(posByNode.items())
offset = sortedPosByNode[0][0] - 1
posList = [offset]
prevNode = offset
for (node, i) in sortedPosByNode:
if prevNode + 1 != node:
good = False
if nonConsecutive == 0:
nonConsecutiveFirst = f"{prevNode} => {node}"
nonConsecutive += 1
else:
posList.append(i)
prevNode = node
posList.append(lastI)

if not good:
return (
f"{nonConsecutive} nonConsecutive nodes, "
f"of which the first one is {nonConsecutiveFirst}"
)
return posList

def write(
self, textPath, inverted=False, posPath=None, byType=False, optimize=True
):
"""Write the recorder information to disk.

The recorded text is written as a plain text file,
and the remembered node positions are written as a tsv file.

You can also let the node positions be written out by node type.
In that case you can also optimize the file size.

Optimization means that consecutive equal values are prepended
by the number of repetitions and a *.

Parameters
----------
textPath: string
The file path to which the accumulated text is written.
inverted: boolean, optional False
If False, the positions are taken as mappings from character
positions to nodes. If True, they are a mapping from nodes to
character positions.
posPath: string, optional None
The file path to which the mapped positions are written.
If absent, it equals textPath with .pos appended, or
.ipos if inverted is True.
The file format is: one line for each character position,
on each line a tab-separated list of active nodes.
byType: boolean, optional False
If True, writes separate node mappings per node type.
For this it is needed that the Recorder has been
passed a TF api when it was initialized.
The file names are extended with the node type.
This extension occurs just before the last . of the inferred posPath.
optimize: boolean, optional True
Optimize file size. Only relevant if byType is True
and inverted is False.
The format of each line is:

*rep* * *nodes

where *rep* is a number that indicates repetition and *nodes*
is a tab-separated list of node numbers.

The meaning is that the following *rep* character positions
are associated with these *nodes*.
"""

textPath = expanduser(textPath)
posExt = ".ipos" if inverted else ".pos"
posPath = expanduser(posPath or f"{textPath}{posExt}")

with open(textPath, "w", encoding="utf8") as fh:
fh.write(self.text())

if not byType:
with open(posPath, "w", encoding="utf8") as fh:
if inverted:
fh.write(
"\n".join(
f"{node}\t{intervals}"
for (node, intervals) in self.iPositions(
byType=False, logical=False, asEntries=True
)
)
)
else:
fh.write(
"\n".join(
"\t".join(str(i) for i in nodes)
for nodes in self.nodesByPos
)
)
return

mapByType = (
self.iPositions(byType=True, logical=False, asEntries=True)
if inverted
else self.positions(byType=True)
)
if mapByType is None:
print("No position files written")
return

(base, ext) = os.path.splitext(posPath)

# if we reach this, there is a TF api

api = self.api
info = api.TF.info
indent = api.TF.indent

indent(level=True, reset=True)

for (nodeType, mapping) in mapByType.items():
fileName = f"{base}-{nodeType}{ext}"
info(f"{nodeType:<20} => {fileName}")
with open(fileName, "w", encoding="utf8") as fh:
if inverted:
fh.write(
"\n".join(
f"{node}\t{intervals}" for (node, intervals) in mapping
)
)
else:
if not optimize:
fh.write(
"\n".join(
"\t".join(str(i) for i in nodes) for nodes in mapping
)
)
else:
repetition = 1
previous = None

for nodes in mapping:
if nodes == previous:
repetition += 1
continue
else:
if previous is not None:
prefix = f"{repetition}*" if repetition > 1 else ""
value = "\t".join(str(i) for i in previous)
fh.write(f"{prefix}{value}\n")
repetition = 1
previous = nodes
if previous is not None:
prefix = f"{repetition + 1}*" if repetition else ""
value = "\t".join(str(i) for i in previous)
fh.write(f"{prefix}{value}\n")

indent(level=False)

Parameters
----------
textPath: string
The file path from which the accumulated text is read.
posPath: string, optional None
The file path from which the mapped positions are read.
If absent, it equals textPath with .pos appended.
The file format is: one line for each character position,
on each line a tab-separated list of active nodes.
"""

textPath = expanduser(textPath)
posPath = expanduser(posPath or f"{textPath}.pos")
self.context = {}

with open(textPath, encoding="utf8") as fh:
self.material = list(fh)

with open(posPath, encoding="utf8") as fh:
self.nodesByPos = [
{int(n) for n in line.rstrip("\n").split("\t")} for line in fh
]

"""Read a tab-separated file of annotation data and convert it to features.

An external annotation tool typically annotates text by assigning values
to character positions or ranges of character positions.

In Text-Fabric, annotations are values assigned to nodes.

If a *recorded* text has been annotated by an external tool,
we can use the position-to-node mapping to construct Text-Fabric features
out of it.

The annotation file is assumed to be a tab-separated file.
Every line corresponds to an annotation.
The first two columns have the start and end positions, as character positions
in the text.
The remaining columns contain annotation values for that strectch of text.

If there is a heading column, the values of the headers translate to names
of the new TF features.

Parameters
----------
featurePath: string
Path to the annotation file.
headers: boolean or iterable, optional True
Indicates whether the annotation file has headers.
If not True, it may be an iterable of names, which will
"""

featurePath = expanduser(featurePath)
nodesByPos = self.nodesByPos

features = {}

with open(featurePath, encoding="utf8") as fh:
names = next(fh).rstrip("\n").split("\t")[2:]
else:
names = None

for line in fh:
(start, end, *data) = line.rstrip("\n").split("\t")
if names is None:
names = tuple(f"f{i}" for i in range(1, len(data) + 1))
nodes = set(
chain.from_iterable(
nodesByPos[i] for i in range(int(start), int(end) + 1)
)
)
for n in nodes:
for i in range(len(names)):
val = data[i]
if not val:
continue
name = names[i]
features.setdefault(name, {})[n] = val

return features

### Instance variables

var context

The currently active nodes.

var material

Accumulated text.

It is a list of chunks of text. The text is just the concatenation of all chunks.

var nodesByPos

Mapping from textual positions to nodes.

It is a list. Entry p in this list stores node information for character position p. That node information consists of the set of active nodes at that position.

### Methods

 def add(self, string, empty='\u200d') 

## Parameters

string : string | None
Material to add. If it is a string, the string will be added to the accumulator. If it is None, a default value will be added. The default value is passed through parameter empty.
empty : string, optional zero-width-joiner
If the string parameter is None, this is the default value that will be added to the accumulator. If this parameter is absent, the zero-width joiner is used.
Expand source code Browse git
def add(self, string, empty=ZWJ):

Parameters
----------
string: string | None
If it is a string, the string will be added to the accumulator.
If it is None, a default value will be added.
The default value is passed through parameter empty.
empty: string, optional zero-width-joiner
If the string parameter is None, this is the default value
that will be added to the accumulator.
If this parameter is absent, the zero-width joiner is used.
"""

if string is None:
string = empty
self.material.append(string)
self.nodesByPos.extend([frozenset(self.context)] * len(string))
 def end(self, n) 

End a node.

That means: delete it from the context, i.e. make the node inactive.

## Parameters

n : integer
A node. The node can be any node type.
Expand source code Browse git
def end(self, n):
"""End a node.

That means: delete it from the context, i.e. make the node inactive.

Parameters
----------
n: integer
A node. The node can be any node type.
"""
self.context.discard(n)
 def iPositions(self, byType=False, logical=True, asEntries=False) 

Get the character positions as mapping from nodes.

## Parameters

byType : boolean, optional False
If True, makes a separate node mapping per node type. For this it is needed that the Recorder has been passed a TF api when it was initialized.
logical : boolean, optional True
If True, specs are represented as tuples of ranges and a range is represented as a tuple of a begin and end point, or as a single point. Points are integers. If False, ranges are represented by strings: , separated ranges, a ranges is b-e or p.
asEntries : boolean, optional False
If True, do not return the dict, but rather its entries.

## Returns

list|dict|None

If byType, the result is a dictionary, keyed by node type, with values the mapping for nodes of that type. Entry n in this mapping contains the intervals of all character positions in the text where node n is active.

If not byType then a single mapping is returned, where each node is mapped to the intervals where that node is active.

Expand source code Browse git
    def iPositions(self, byType=False, logical=True, asEntries=False):
"""Get the character positions as mapping from nodes.

Parameters
----------
byType: boolean, optional False
If True, makes a separate node mapping per node type.
For this it is needed that the Recorder has been
passed a TF api when it was initialized.
logical: boolean, optional True
If True, specs are represented as tuples of ranges
and a range is represented as a tuple of a begin and end point,
or as a single point.
Points are integers.
If False, ranges are represented by strings: , separated ranges,
a ranges is b-e or p.
asEntries: boolean, optional False
If True, do not return the dict, but rather its entries.

Returns
-------
list|dict|None
If byType, the result is a dictionary, keyed by node type,
with values the mapping for nodes of that type.
Entry n in this mapping contains the intervals of all
character positions in the text where node n is active.

If not byType then a single mapping is returned, where each node
is mapped to the intervals where that node is active.
"""

method = specFromRangesLogical if logical else specFromRanges
posByNode = {}
for (i, nodeSet) in enumerate(self.nodesByPos):
for node in nodeSet:
for (n, posSet) in posByNode.items():
posByNode[n] = method(rangesFromSet(posSet))

if asEntries:
posByNode = tuple(posByNode.items())
if not byType:
return posByNode

api = self.api
if api is None:
print(
"""\
Cannot determine node types without a TF api.
You have to call Recorder(api) instead of Recorder()
where api is the result of
tf.app.use(corpus)
or
"""
)
return None

F = api.F
Fotypev = F.otype.v

posByNodeType = {}
if asEntries:
for (n, spec) in posByNode:
nType = Fotypev(n)
posByNodeType.setdefault(nType, []).append((n, spec))
else:
for (n, spec) in posByNode.items():
nType = Fotypev(n)
posByNodeType.setdefault(nType, {})[n] = spec

return posByNodeType
 def makeFeatures(self, featurePath, headers=True) 

Read a tab-separated file of annotation data and convert it to features.

An external annotation tool typically annotates text by assigning values to character positions or ranges of character positions.

In Text-Fabric, annotations are values assigned to nodes.

If a recorded text has been annotated by an external tool, we can use the position-to-node mapping to construct Text-Fabric features out of it.

The annotation file is assumed to be a tab-separated file. Every line corresponds to an annotation. The first two columns have the start and end positions, as character positions in the text. The remaining columns contain annotation values for that strectch of text.

If there is a heading column, the values of the headers translate to names of the new TF features.

## Parameters

featurePath : string
Path to the annotation file.
headers : boolean or iterable, optional True
Indicates whether the annotation file has headers. If not True, it may be an iterable of names, which will be used as headers.
Expand source code Browse git
def makeFeatures(self, featurePath, headers=True):
"""Read a tab-separated file of annotation data and convert it to features.

An external annotation tool typically annotates text by assigning values
to character positions or ranges of character positions.

In Text-Fabric, annotations are values assigned to nodes.

If a *recorded* text has been annotated by an external tool,
we can use the position-to-node mapping to construct Text-Fabric features
out of it.

The annotation file is assumed to be a tab-separated file.
Every line corresponds to an annotation.
The first two columns have the start and end positions, as character positions
in the text.
The remaining columns contain annotation values for that strectch of text.

If there is a heading column, the values of the headers translate to names
of the new TF features.

Parameters
----------
featurePath: string
Path to the annotation file.
headers: boolean or iterable, optional True
Indicates whether the annotation file has headers.
If not True, it may be an iterable of names, which will
"""

featurePath = expanduser(featurePath)
nodesByPos = self.nodesByPos

features = {}

with open(featurePath, encoding="utf8") as fh:
names = next(fh).rstrip("\n").split("\t")[2:]
else:
names = None

for line in fh:
(start, end, *data) = line.rstrip("\n").split("\t")
if names is None:
names = tuple(f"f{i}" for i in range(1, len(data) + 1))
nodes = set(
chain.from_iterable(
nodesByPos[i] for i in range(int(start), int(end) + 1)
)
)
for n in nodes:
for i in range(len(names)):
val = data[i]
if not val:
continue
name = names[i]
features.setdefault(name, {})[n] = val

return features
 def positions(self, byType=False, simple=False) 

Get the node positions as mapping from character positions.

## Parameters

byType : boolean, optional False
If True, makes a separate node mapping per node type. For this it is needed that the Recorder has been passed a TF api when it was initialized.
simple : boolean, optional False
In some cases it is known on beforehand that at each textual position there is at most 1 node. Then it is more economical to fill the list with single nodes rather than with sets of nodes. If this parameter is True, we pick the first node from the set.

## Returns

list|dict|None

If byType, the result is a dictionary, keyed by node type, with values the mapping of textual positions to nodes of that type. This mapping takes the shape of a list where entry i contains the frozen set of all nodes of that type that were active at character position i in the text.

If not byType then a single mapping is returned (as list), where entry i contains the frozen set of all nodes, irrespective of their type that were active at character position i in the text.

Expand source code Browse git
    def positions(self, byType=False, simple=False):
"""Get the node positions as mapping from character positions.

Parameters
----------
byType: boolean, optional False
If True, makes a separate node mapping per node type.
For this it is needed that the Recorder has been
passed a TF api when it was initialized.
simple: boolean, optional False
In some cases it is known on beforehand that at each textual position
there is at most 1 node.
Then it is more economical to fill the list with single nodes
rather than with sets of nodes.
If this parameter is True, we pick the first node from the set.

Returns
-------
list|dict|None
If byType, the result is a dictionary, keyed by node type,
with values the mapping of textual positions to nodes of that type.
This mapping takes the shape of a list where entry i
contains the frozen set of all nodes of that type
that were active at character position i in the text.

If not byType then a single mapping is returned (as list),
where entry i contains the frozen set of all
nodes, irrespective of their type
that were active at character position i in the text.
"""

if not byType:
if simple:
return tuple(list(x)[0] if x else None for x in self.nodesByPos)
return self.nodesByPos

api = self.api
if api is None:
print(
"""\
Cannot determine node types without a TF api.
You have to call Recorder(api) instead of Recorder()
where api is the result of
tf.app.use(corpus)
or
"""
)
return None

F = api.F
Fotypev = F.otype.v
info = api.TF.info
indent = api.TF.indent

indent(level=True, reset=True)
info("gathering nodes ...")

allNodes = set(chain.from_iterable(self.nodesByPos))
allTypes = {Fotypev(n) for n in allNodes}
info(f"found {len(allNodes)} nodes in {len(allTypes)} types")

nodesByPosByType = {nodeType: [] for nodeType in allTypes}

info("partitioning nodes over types ...")

for nodeSet in self.nodesByPos:
typed = {}
for node in nodeSet:
nodeType = Fotypev(node)
for nodeType in allTypes:
thisSet = (
frozenset(typed[nodeType]) if nodeType in typed else frozenset()
)
value = (list(thisSet)[0] if thisSet else None) if simple else thisSet
nodesByPosByType[nodeType].append(value)

info("done")
indent(level=False)
return nodesByPosByType
 def rPositions(self, acceptMaterialOutsideNodes=False) 

Get the first textual position for each node

The position information is a big amount of data, in the general case. Under certain assumptions we can economize on this data usage.

Strong assumptions:

1. every textual position is covered by exactly one node
2. the nodes are consecutive: every next node is equal to the previous node plus 1
3. the positions of the nodes are monotonous in the nodes, i.e. if node n < m, then the position of n is before the position of m.

Imagine the text partitioned in consecutive non-overlapping chunks, where each node corresponds to exactly one chunk, and the order of the nodes is the same as the order of the corresponding chunks.

## Parameters

acceptMaterialOutsideNodes : boolean, optional False

If this is True, we accept that the text contains extra material that is not covered by any node. So condition 1 above is relaxed in that we accept no nodes for a textual position.

Applications that make use of the positions must realize that in this case the material associated with a node also includes the subsequent material outside any node.

## Returns

list | str

The result is a list posList. Suppose we map nodes n, n+1, …, n+m. Suppose node n starts at position t0, n+1 at position t1, n+m at position tm. Suppose te is the position just after the whole text.

Then we deliver as a result:

n - 1 t0 t1 ... tm te

So the first element of the list is used to specify the offset to be applied for all subsequent nodes. The te value is added as a sentinel, to facilitate the determination of the last position of each node.

Users of posList can find the start and end positions of node m as follows

start = posList[m - posList[0]] end = posList[m - posList[0] + 1] - 1

We return not only the posList if the assumptions hold. If not, we return a string with diagnostic information.

Expand source code Browse git
def rPositions(self, acceptMaterialOutsideNodes=False):
"""Get the first textual position for each node

The position information is a big amount of data, in the general case.
Under certain assumptions we can economize on this data usage.

Strong assumptions:

1.  every textual position is covered by **exactly one node**
2.  the nodes are consecutive:
every next node is equal to the previous node plus 1
3.  the positions of the nodes are monotonous in the nodes, i.e.
if node n < m, then the position of n is before the position of m.

Imagine the text partitioned in consecutive non-overlapping chunks, where
each node corresponds to exactly one chunk, and the order of the nodes
is the same as the order of the corresponding chunks.

Parameters
----------
acceptMaterialOutsideNodes: boolean, optional False
If this is True, we accept that the text contains extra material that is not
covered by any node.
So condition 1 above is relaxed in that we accept no nodes for a textual
position.

Applications that make use of the positions must realize that in this case
the material associated with a node also includes the subsequent material
outside any node.

Returns
-------
list | str
The result is a list posList.
Suppose we map nodes n, n+1, ..., n+m.
Suppose node n starts at position t0, n+1 at position t1,
n+m at position tm.
Suppose te is the position just after the whole text.

Then we deliver as a result:


n - 1
t0
t1
...
tm
te


So the first element of the list is used to specify the offset to be
applied for all subsequent nodes.
The te value is added as a sentinel, to facilitate the determination
of the last position of each node.

Users of posList can find the start and end positions of node m
as follows


start = posList[m - posList[0]]
end   = posList[m - posList[0] + 1] - 1


We return not only the posList if the assumptions hold.
If not, we return a string with diagnostic information.
"""

good = True
multipleNodes = 0
multipleFirst = 0
noNodes = 0
noFirst = 0
nonConsecutive = 0
nonConsecutiveFirst = 0

posByNode = {}
for (i, nodeSet) in enumerate(self.nodesByPos):
if (not acceptMaterialOutsideNodes and len(nodeSet) == 0) or len(
nodeSet
) > 1:
good = False
if len(nodeSet) == 0:
if noNodes == 0:
noFirst = i
noNodes += 1
else:
if multipleNodes == 0:
multipleFirst = i
multipleNodes += 1
continue
for node in nodeSet:
if node in posByNode:
continue
posByNode[node] = i

lastI = i

if not good:
msg = ""
if noNodes:
msg += (
f"{noNodes} positions without node, "
f"of which the first one is {noFirst}\n"
)
if multipleNodes:
msg += (
f"{multipleNodes} positions with multiple nodes, "
f"of which the first one is {multipleFirst}\n"
)
return msg

sortedPosByNode = sorted(posByNode.items())
offset = sortedPosByNode[0][0] - 1
posList = [offset]
prevNode = offset
for (node, i) in sortedPosByNode:
if prevNode + 1 != node:
good = False
if nonConsecutive == 0:
nonConsecutiveFirst = f"{prevNode} => {node}"
nonConsecutive += 1
else:
posList.append(i)
prevNode = node
posList.append(lastI)

if not good:
return (
f"{nonConsecutive} nonConsecutive nodes, "
f"of which the first one is {nonConsecutiveFirst}"
)
return posList
 def read(self, textPath, posPath=None) 

## Parameters

textPath : string
The file path from which the accumulated text is read.
posPath : string, optional None
The file path from which the mapped positions are read. If absent, it equals textPath with .pos appended. The file format is: one line for each character position, on each line a tab-separated list of active nodes.
Expand source code Browse git
def read(self, textPath, posPath=None):

Parameters
----------
textPath: string
The file path from which the accumulated text is read.
posPath: string, optional None
The file path from which the mapped positions are read.
If absent, it equals textPath with .pos appended.
The file format is: one line for each character position,
on each line a tab-separated list of active nodes.
"""

textPath = expanduser(textPath)
posPath = expanduser(posPath or f"{textPath}.pos")
self.context = {}

with open(textPath, encoding="utf8") as fh:
self.material = list(fh)

with open(posPath, encoding="utf8") as fh:
self.nodesByPos = [
{int(n) for n in line.rstrip("\n").split("\t")} for line in fh
]
 def start(self, n) 

Start a node.

That means: add it to the context, i.e. make the node active.

## Parameters

n : integer
A node. The node can be any node type.
Expand source code Browse git
def start(self, n):
"""Start a node.

That means: add it to the context, i.e. make the node active.

Parameters
----------
n: integer
A node. The node can be any node type.
"""
self.context.add(n)
 def text(self) 

Get the accumulated text.

## Returns

string
The join of all accumulated text chunks.
Expand source code Browse git
def text(self):
"""Get the accumulated text.

Returns
-------
string
The join of all accumulated text chunks.
"""
return "".join(self.material)
 def write(self, textPath, inverted=False, posPath=None, byType=False, optimize=True) 

Write the recorder information to disk.

The recorded text is written as a plain text file, and the remembered node positions are written as a tsv file.

You can also let the node positions be written out by node type. In that case you can also optimize the file size.

Optimization means that consecutive equal values are prepended by the number of repetitions and a *.

## Parameters

textPath : string
The file path to which the accumulated text is written.
inverted : boolean, optional False
If False, the positions are taken as mappings from character positions to nodes. If True, they are a mapping from nodes to character positions.
posPath : string, optional None
The file path to which the mapped positions are written. If absent, it equals textPath with .pos appended, or .ipos if inverted is True. The file format is: one line for each character position, on each line a tab-separated list of active nodes.
byType : boolean, optional False
If True, writes separate node mappings per node type. For this it is needed that the Recorder has been passed a TF api when it was initialized. The file names are extended with the node type. This extension occurs just before the last . of the inferred posPath.
optimize : boolean, optional True

Optimize file size. Only relevant if byType is True and inverted is False. The format of each line is:

rep * *nodes

where rep is a number that indicates repetition and nodes is a tab-separated list of node numbers.

The meaning is that the following rep character positions are associated with these nodes.

Expand source code Browse git
def write(
self, textPath, inverted=False, posPath=None, byType=False, optimize=True
):
"""Write the recorder information to disk.

The recorded text is written as a plain text file,
and the remembered node positions are written as a tsv file.

You can also let the node positions be written out by node type.
In that case you can also optimize the file size.

Optimization means that consecutive equal values are prepended
by the number of repetitions and a *.

Parameters
----------
textPath: string
The file path to which the accumulated text is written.
inverted: boolean, optional False
If False, the positions are taken as mappings from character
positions to nodes. If True, they are a mapping from nodes to
character positions.
posPath: string, optional None
The file path to which the mapped positions are written.
If absent, it equals textPath with .pos appended, or
.ipos if inverted is True.
The file format is: one line for each character position,
on each line a tab-separated list of active nodes.
byType: boolean, optional False
If True, writes separate node mappings per node type.
For this it is needed that the Recorder has been
passed a TF api when it was initialized.
The file names are extended with the node type.
This extension occurs just before the last . of the inferred posPath.
optimize: boolean, optional True
Optimize file size. Only relevant if byType is True
and inverted is False.
The format of each line is:

*rep* * *nodes

where *rep* is a number that indicates repetition and *nodes*
is a tab-separated list of node numbers.

The meaning is that the following *rep* character positions
are associated with these *nodes*.
"""

textPath = expanduser(textPath)
posExt = ".ipos" if inverted else ".pos"
posPath = expanduser(posPath or f"{textPath}{posExt}")

with open(textPath, "w", encoding="utf8") as fh:
fh.write(self.text())

if not byType:
with open(posPath, "w", encoding="utf8") as fh:
if inverted:
fh.write(
"\n".join(
f"{node}\t{intervals}"
for (node, intervals) in self.iPositions(
byType=False, logical=False, asEntries=True
)
)
)
else:
fh.write(
"\n".join(
"\t".join(str(i) for i in nodes)
for nodes in self.nodesByPos
)
)
return

mapByType = (
self.iPositions(byType=True, logical=False, asEntries=True)
if inverted
else self.positions(byType=True)
)
if mapByType is None:
print("No position files written")
return

(base, ext) = os.path.splitext(posPath)

# if we reach this, there is a TF api

api = self.api
info = api.TF.info
indent = api.TF.indent

indent(level=True, reset=True)

for (nodeType, mapping) in mapByType.items():
fileName = f"{base}-{nodeType}{ext}"
info(f"{nodeType:<20} => {fileName}")
with open(fileName, "w", encoding="utf8") as fh:
if inverted:
fh.write(
"\n".join(
f"{node}\t{intervals}" for (node, intervals) in mapping
)
)
else:
if not optimize:
fh.write(
"\n".join(
"\t".join(str(i) for i in nodes) for nodes in mapping
)
)
else:
repetition = 1
previous = None

for nodes in mapping:
if nodes == previous:
repetition += 1
continue
else:
if previous is not None:
prefix = f"{repetition}*" if repetition > 1 else ""
value = "\t".join(str(i) for i in previous)
fh.write(f"{prefix}{value}\n")
repetition = 1
previous = nodes
if previous is not None:
prefix = f"{repetition + 1}*" if repetition else ""
value = "\t".join(str(i) for i in previous)
fh.write(f"{prefix}{value}\n")

indent(level=False)